21 professor-computer-science-"https:"-"https:"-"https:"-"https:"-"Dr" PhD positions at Cranfield University
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at Cranfield University within a supportive, cross-disciplinary environment spanning energy systems engineering, computational modelling, bioinformatics and food safety science. The studentship is funded by
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reduces crack propagation in composites, reduce failure due to delamination and significantly improves fracture toughness [Williams et al, Journal of Materials Science 48, 3, 1005-1013, 2013]. In addition
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second class UK honours degree or equivalent in a related discipline. This project would suit students with an aerospace or mechanical engineering background. Experience of computational fluid dynamics
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detection of chemical and microbial contaminants in rivers. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research
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including the Ground Operations Laboratory in the Digital Aviation Research and Technology Centre (DARTeC), and the suite of facilities in the new Cranfield Hydrogen Integration Incubator (CH2i
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fees. Diversity and Inclusion at Cranfield We are committed to fostering equity, diversity, and inclusion in our CDT program, and warmly encourage applications from students of all backgrounds, including
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are part of the programme. Entry requirements Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit students with an aerospace
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infrastructure. This project sits at the interface of environmental engineering, water quality management, and sustainable infrastructure. This PhD project will explore how ICWs can be strengthened through design
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap